AGIBOT releases GE-Act 2.0 — the first native World Action Model to validate a pretraining and scaling path for embodied AI. 📖 Explore the project: Trained entirely from scratch on embodied manipulation data: visual representation, future generation, and action prediction, all from random initialization. No inherited video generators. No task-specific fine-tuning. Put straight to a ruthless real-robot zero-shot test — unseen scenes, unseen objects, 100 atomic tasks, 20 skill categories, and two robot embodiments: ✅ Data scaled 100×: from 300 to 30,000 hours ✅ Task success climbs from 17.1% to 44.1% on G1-OP — with no sign of saturation ✅ New skills emerge at scale: folding towels, nesting paper cups, uncapping pens, arranging flowers ✅ Cross-embodiment transfer: G2-90D, under 2% of the training data, still gains 17.7 percentage points ✅ Failure data becomes a training asset — 2,000 hours of failed manipulations and deployment rollouts A capable model envisions reality before it acts. #AGIBOT# #EmbodiedAI# #WorldModel# #PhysicalAI# #Robotics#